Different methods for quantitative analysis of three-dimensional (3D) and four-dimensional (4D) echocardiograms are known. With 3D and 4D echocardiography, different image slices may be extracted from acquired volumes, which may be acquired as a series of image frames covering the cardiac cycle.
In 4D echocardiography, for example, a sequence of volumetric images of a patient's heart may be acquired using an ultrasound system. Compared to conventional 2D echocardiography, 4D echocardiography increases the complexity of visualization and analysis of the acquired data. Thus, a high degree of manual interaction often has to be performed to extract clinically useful information. Typical examples of such manual interaction include cropping of volumetric data for visualization, such as for optimal visualization of the cardiac wall. Further, manual placement of regions of interest (ROIs) may be used. Thus, a high level of input from the user may be needed.
For example, the interventricular septum thickness (IVSd) is one screening measurement in echocardiography, as this thickness, along with left ventricle (LV) size, may be used as a screening parameter for septal hypertrophy, and also shows a correlation to 24 hour ambulatory blood pressure. In particular, as cardiac hypertrophy potentially leads to other cardiac complications, the measurement can be used for screening purposes. In general, heart wall and chamber dimensions may be used as screening parameters for detection of cardiac diseases. However, because the measurements are performed manually, inter- and intra-observer variability occurs as a result of observer variability based on experience and expertise.
Automation of the workflow for septum thickness measurement is challenging and known methods may not perform satisfactorily for use in real time. Within the workflow, identifying the septum border is an important and challenging first step. In particular, noise and in-homogeneities induced by near field haze represent challenges for achieving a good segmentation result. These challenges result in the need for algorithms that advance the initialization closer to the septum boundary. However, in some cases, such as where the septum boundary has low contrast resulting in large boundary gaps, the known segmentation methods fail or fail to perform satisfactorily.
Moreover, other approaches, such as region based active contour approaches, also suffer from drawbacks, such as the infeasibility of generating shape atlases for the septum given the large inter-patient shape variability and non-rigid deformation across frames, as well as failure of the segmentation when constraining the width where the septum boundary has low contrast.